Mental health symptom burden in elite ice hockey players and its association with self-reported concussive events
Bibliographic record
Abstract
BACKGROUND: Some studies suggest that elite athletes experience adverse mental health symptoms at rates commensurate with the general population, despite the well-established buffering effects of exercise. Within contact sports, such as ice-hockey, recurrent concussions may be a source of this discrepancy. We compared the point prevalence of various mental health outcomes with other athlete and general population samples, as well as investigated their relationship with concussive events. METHODS: We surveyed 648 active ice hockey players from the top two men's tiers and the top women's tier in Swedish elite ice hockey on lifetime concussive events, hazardous alcohol use, problematic social media use, depression, anxiety, and burnout. RESULTS: Hazardous alcohol use was more prevalent among male ice hockey players (29.5% AUDIT-C ≥ 6) compared to other athlete and general population samples, while other mental health symptoms were less common. Female ice hockey players reported higher hazardous alcohol consumption (36.4% AUDIT-C ≥ 4) than another athlete sample and more burnout (19.1%) than the general population. After adjusting for covariates, athletes with 3+ concussive events had 2.1 times the odds of elevated depressive symptoms and 3.5 times the odds of elevated burnout symptoms compared to those with no concussion history. Treating lifetime concussive events as a continuous predictor revealed positive correlations with all outcomes except for hazardous alcohol use. CONCLUSIONS: Mental health outcome rates among active elite ice hockey athletes differ from those of other athlete and general population samples, whilst concussive events may be particularly linked to elevated symptoms of depression and burnout.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".